{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 皮马印第安人糖尿病发病预测\n",
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据並查看数据信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train : (768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train :\", train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose            5\n",
      "BloodPressure     35\n",
      "SkinThickness    227\n",
      "Insulin          374\n",
      "BMI               11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看数据分布及相关性\n",
    "### 发病情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Outcome'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 怀孕次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "### Number of occurrences\n",
    "sns.countplot(train['Pregnancies'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8c5330310>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Pregnancies\", hue=\"Outcome\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 血浆葡萄糖浓度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train['Glucose'], kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Glucose', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 血压"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train['BloodPressure'], kde = False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='BloodPressure', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('blood_pressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三头肌皮褶厚度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train['SkinThickness'], kde = False)\n",
    "plt.xlabel('Skin_fold_thickness')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='SkinThickness', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Triceps_skin_fold_thickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 餐后血清胰岛素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train['Insulin'], kde = False)\n",
    "plt.xlabel('serum_insulin')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Insulin', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('serum_insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZYFPNLsnJwNaq2jB99Qybjkzm1glVdTTNNO05SU4cdqAOlgNHAx+rqqOAnzJCU0JzafcZvQq4YqGvsZTLYJwve/FAkv0B2tutQ87zJEmeQlMEn6uqq9rVI597SlU9DNxEs89jzyRTJ2CO2ufkBOBVSe6lucLvapqRwihnpqrua2+30sxhH8Pofz62AFuqan27fCVNOYx6bmhK9+aqeqBd3u7MS7kMxvmyF9cAa9r7a2jm5EdGkgAXAZuq6v3THhr13CuS7Nne3w14Kc0OwhuB09rNRip3VV1QVQdW1Uqaz/BXquq1jHDmJHskefrUfZq57NsZ8c9HVf0Q+H6Sw9pVJwHfZsRzt17Dr6eIYCGZh73To+cdKq8EvkszL/yuYeeZJeOlwP3AL2j+Z3IWzZzwOuCu9nbvYefcJvPv0kxL/Bewsf155Rjk/h3gljb37cBft+sPAb4J3E0zzH7qsLPOkv/FwLWjnrnNdmv7c8fU371R/3y0GY8EJtrPyJeAvUY9N83BED8CfnPauu3O7OUoJElLeppIktSRZSBJsgwkSZaBJAnLQJKEZSDNKckT7dUgb01yc5IXtetXJqkk75227b5JfpHkH9vl9yR527CyS9vDMpDm9mg1V4U8guZih3837bF7gJOnLZ9Oc1y9NHYsA6m7Z9BcLnrKo8CmJFOXaH418PmBp5IWwfL5N5F2aru1VzndleZSwKu3efwy4IwkPwSeoLlG0LMGG1HacZaBNLdHq7nKKUmOBz6T5AXTHr8OeC/wAHD5EPJJi8JpIqmjqvoGsC+wYtq6x4ANwFtpruIqjSVHBlJHSZ4HLKO5KNju0x76B+CrVfWj5oKu0vixDKS5Te0zgOZLZdZU1RPT/9GvqjvwKCKNOa9aKklyn4EkyTKQJGEZSJKwDCRJWAaSJCwDSRKWgSQJ+H+BzjvxzJMc9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train['BMI'], kde = False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='BMI', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8c66f8f90>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train.groupby(['BMI', 'Outcome'])['BMI'].count().unstack('Outcome').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 糖尿病家系作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Diabetes_pedigree_function，糖尿病家系作用\n",
    "fig = plt.figure()\n",
    "sns.distplot(train['DiabetesPedigreeFunction'], kde = False)\n",
    "plt.xlabel('Diabetes_pedigree_function')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8c6ce4990>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['DiabetesPedigreeFunction', 'Outcome'])['DiabetesPedigreeFunction'].count().unstack('Outcome').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='DiabetesPedigreeFunction', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('DiabetesPedigreeFunction', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train['Age'], kde = False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8c83c8250>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Age', 'Outcome'])['Age'].count().unstack('Outcome').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Age', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Age', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8c8829a50>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程\n",
    "### 处理缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                   0\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "Outcome                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train[NaN_col_names] = train[NaN_col_names].replace(0, np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>SkinThickness_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SkinThickness  SkinThickness_Missing\n",
       "0           35.0                      0\n",
       "1           29.0                      0\n",
       "2            NaN                      1\n",
       "3           23.0                      0\n",
       "4           35.0                      0\n",
       "5            NaN                      1\n",
       "6           32.0                      0\n",
       "7            NaN                      1\n",
       "8           45.0                      0\n",
       "9            NaN                      1"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SkinThickness_Missing'] = train['SkinThickness'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['SkinThickness','SkinThickness_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8c8d95e90>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"SkinThickness_Missing\", hue=\"Outcome\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8c8ff6490>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Insulin_Missing'] = train['Insulin'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "sns.countplot(x=\"Insulin_Missing\", hue=\"Outcome\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing = train[['Insulin_Missing','SkinThickness_Missing']]\n",
    "train.drop([\"SkinThickness_Missing\", \"Insulin_Missing\"], axis=1, inplace=True)\n",
    "medians = train.median() \n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Insulin_Missing</th>\n",
       "      <th>SkinThickness_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0     0.639947  0.866045      -0.031990       0.670643 -0.181541  0.166619   \n",
       "1    -0.844885 -1.205066      -0.528319      -0.012301 -0.181541 -0.852200   \n",
       "2     1.233880  2.016662      -0.693761      -0.012301 -0.181541 -1.332500   \n",
       "3    -0.844885 -1.073567      -0.528319      -0.695245 -0.540642 -0.633881   \n",
       "4    -1.141852  0.504422      -2.679076       0.670643  0.316566  1.549303   \n",
       "\n",
       "   DiabetesPedigreeFunction       Age  Outcome  Insulin_Missing  \\\n",
       "0                  0.468492  1.425995        1                1   \n",
       "1                 -0.365061 -0.190672        0                1   \n",
       "2                  0.604397 -0.105584        1                1   \n",
       "3                 -0.920763 -1.041549        0                0   \n",
       "4                  5.484909 -0.020496        1                0   \n",
       "\n",
       "   SkinThickness_Missing  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      1  \n",
       "3                      0  \n",
       "4                      0  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  get labels\n",
    "y_train = train['Outcome']   \n",
    "\n",
    "X_train = train.drop([\"Outcome\"], axis=1)\n",
    "\n",
    "#用于保存特征工程之后的结果\n",
    "feat_names = X_train.columns\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "\n",
    "#存为csv格式\n",
    "X_train = pd.DataFrame(columns = feat_names, data = X_train)\n",
    "train = pd.concat([X_train, y_train, missing], axis = 1)\n",
    "train.to_csv('FE_pima-indians-diabetes.csv',index = False,header=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic 回归\n",
    "### 默认参数的Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.48805562 0.53007069 0.45629158 0.42247552 0.48388972]\n",
      "cv logloss is: 0.47615662604739956\n",
      "accuracy of each fold is::  [0.75974026 0.74675325 0.78571429 0.79738562 0.77124183]\n",
      "cv accuracy is:  0.7721670486376369\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(max_iter=1000)\n",
    "from sklearn.model_selection import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "accuracy = cross_val_score(lr, X_train, y_train, cv=5, scoring='accuracy')\n",
    "#%timeit loss_sparse = cross_val_score(lr, X_train_sparse, y_train, cv=3, scoring='neg_log_loss')\n",
    "print ('logloss of each fold is: ',-loss)\n",
    "print ('cv logloss is:', (-loss).mean())\n",
    "print ('accuracy of each fold is:: ', accuracy)\n",
    "print ('cv accuracy is: ', accuracy.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression + GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='liblinear',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=4,\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000], 'penalty': ['l1', 'l2']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [ 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression(solver='liblinear')\n",
    "# log似然损失\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss',n_jobs = 4,return_train_score=True)\n",
    "grid.fit(X_train,y_train)\n",
    "# 正确率\n",
    "grid2= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='accuracy',n_jobs = 4,return_train_score=True)\n",
    "grid2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47596856742135135\n",
      "{'C': 1, 'penalty': 'l1'}\n",
      "0.7747644512350395\n",
      "{'C': 0.1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "print(grid2.best_score_)\n",
    "print(grid2.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "排除 ['Insulin_Missing','SkinThickness_Missing'] 的结果  \n",
    "0.4759694037030119  \n",
    "{'C': 1, 'penalty': 'l1'}  \n",
    "0.7747644512350395  \n",
    "{'C': 0.1, 'penalty': 'l2'}    \n",
    "  \n",
    "包含 ['Insulin_Missing','SkinThickness_Missing'] 的结果   \n",
    "0.4759681787718638  \n",
    "{'C': 1, 'penalty': 'l1'}  \n",
    "0.7747644512350395  \n",
    "{'C': 0.1, 'penalty': 'l2'}  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ai_1",
   "language": "python",
   "name": "ai_1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
